code: def test_overall():
      gpyopt = GPyOpt(max_concurrent=1)
      (
          parameters,
          results,
          lower_is_better,
      ) = sherpa.algorithms.get_sample_results_and_params()
  
      for i in range(51):
          suggestion = gpyopt.get_suggestion(
              parameters,
              results.loc[results["Trial-ID"] < i, :],
              lower_is_better,
          )
          print(suggestion)
  
imports: import sherpa
  import pandas
  import pytest
  import numpy
  import collections
  import GPyOpt
  from sherpa.algorithms.bayesian_optimization import GPyOpt
  from sherpa.algorithms import Repeat
label_desc: Use image node_id 17 for calling a global function with example usage: test_overall() without return types